Thursday, February 28, 2013

I recently came across this interview (thanks Dharini for the link!) with Nick Chamandy, a statistician a.k.a a data scientist at Google. I would encourage you to read it; it does have some great points. I found the following snippets interesting:

Recruiting data scientists:

When posting job opportunities, we are cognizant that people from different academic fields tend to use different language, and we don’t want to miss out on a great candidate because he or she comes from a non-statistics background and doesn’t search for the right keyword. On my team alone, we have had successful “statisticians” with degrees in statistics, electrical engineering, econometrics, mathematics, computer science, and even physics. All are passionate about data and about tackling challenging inference problems.

I share the same view. The best scientists I have met are not statisticians by academic training. They are domain experts and design thinkers and they all share one common trait: they love data! When asked how they might build a team of data scientists I highly recommend people to look beyond traditional wisdom. You will be in good shape as long as you don't end up in a situation like this :-)

Skills:

The engineers at Google have also developed a truly impressive package for massive parallelization of R computations on hundreds or thousands of machines. I typically use shell or python scripts for chaining together data aggregation and analysis steps into “pipelines.”

Most companies won't have the kind of highly skilled development army that Google has but then not all companies would have Google scale problem to deal with. Though I suggest two things: a) build a very strong community of data scientists using social tools so that they can collaborate on challenges and tools they use b) make sure that the chief data scientist (if you have one) has very high level of management buy-in to make things happen otherwise he/she would be spending all the time in "alignment" meetings as opposed to doing the real work.

Data preparation:

There is a strong belief that without becoming intimate with the raw data structure, and the many considerations involved in filtering, cleaning, and aggregating the data, the statistician can never truly hope to have a complete understanding of the data.

I disagree. I do strongly believe the tools need to involve to do some of these things and the data scientists should not be spending their time to compensate for the inefficiencies of the tools. Becoming intimate with the data—have empathy for the problem—is certainly a necessity but spending time on pulling, fixing, and aggregating data is not the best use of their time.

Attitude:

To me, it is less about what skills one must brush up on, and much more about a willingness to adaptively learn new skills and adjust one’s attitude to be in tune with the statistical nuances and tradeoffs relevant to this New Frontier of statistics.

As I would say bring tools and knowledge but leave bias and expectations aside. The best data scientists are the ones who are passionate about data, can quickly learn a new domain, and are willing to make and fail and fail and make.

Friday, February 15, 2013

My ongoing conversations with several people continue to reaffirm my belief that Data Science is still perceived to be a sacred discipline and data scientists are perceived to be highly skilled statisticians who walk around wearing white lab coats. The best data scientists are not the ones who know the most about data but they are the ones who are flexible enough to take on any domain with their curiosity to unearth insights. Apparently this is not well-understood. There are two parts to data science: domain and algorithms or in other words knowledge about the problem and knowledge about how to solve it.

One of the main aspects of Big Data that I get excited about is an opportunity to commoditize this data science—the how—by making it mainstream.

The rise of interest in Big Data platform—disruptive technology and desire to do something interesting about data—opens up opportunities to write some of these known algorithms that are easy to execute without any performance penalty. Run K Means if you want and if you don't like the result run Bayesian linear regression or something else. The access to algorithms should not be limited to the "scientists," instead any one who wants to look at their data to know the unknown should be able to execute those algorithms without any sophisticated training, experience, and skills. You don't have to be a statistician to find a standard deviation of a data set. Do you really have to be a statistician to run a classification algorithm?

Data science should not be a sacred discipline and data scientists shouldn't be voodoos.

There should not be any performance penalty or an upfront hesitation to decide what to do with data. People should be able to iterate as fast as possible to get to the result that they want without worrying about how to set up a "data experiment." Data scientists should be design thinkers.

So, what about traditional data scientists? What will they do?

I expect people that are "scientists" in a traditional sense would elevate themselves in their Maslow's hierarchy by focusing more on advanced aspects of data science and machine learning such as designing tools that would recommend algorithms that might fit the data (we have already witnessed this trend for visualization). There's also significant potential to invent new algorithms based on existing machine learning algorithms that have been into existence for a while. What algorithms to execute when could still be a science to some extent but that's what the data scientists should focus on and not on sampling, preparing, and waiting for hours to analyze their data sets. We finally have Big Data for that.